Microsoft AI-300 Questions & Answers - in .pdf
- Total Q&A: 159
- Update: Aug 08, 2026
- Price: $59.99
- Vendor: Microsoft
- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
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Microsoft AI-300 Q&A - Testing Engine
- Total Q&A: 159
- Update: Aug 08, 2026
- Price: $59.99
- Vendor: Microsoft
- Exam Code: AI-300
- Exam Name: Operationalizing Machine Learning and Generative AI Solutions
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Microsoft AI-300 Exam Overview:
| Certification Vendor: | Microsoft |
| Exam Name: | Operationalizing Machine Learning and Generative AI Solutions |
| Exam Number: | AI-300 |
| Certificate Validity Period: | 1 year (renewable) |
| Exam Price: | $165 USD |
| Related Certifications: | Machine Learning Operations (MLOps) Engineer Associate |
| Exam Duration: | 100-120 |
| Real Exam Qty: | 40-60 |
| Exam Format: | Multiple choice, Multiple response, Drag and drop, Case study, Build list |
| Available Languages: | English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Italian, Indonesian (Indonesia), Chinese (Traditional) |
| Passing Score: | 700/1000 |
| Sample Questions: | Microsoft AI-300 Sample Questions |
| Exam Way: | Online (proctored via Pearson VUE) or at a Pearson VUE testing center |
| Pre Condition: | Candidates should have subject matter expertise in setting up infrastructure for MLOps and GenAIOps solutions on Azure, with experience in training, deploying, and maintaining ML models using Azure Machine Learning and generative AI applications using Microsoft Foundry. No formal prerequisite exam is required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-300 |
Microsoft AI-300 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets |
| Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Evaluate generative AI outputs for quality, safety, and grounding - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering |
| Optimize generative AI systems and model performance | - Implement cost management and scaling strategies for GenAI workloads - Optimize inference performance, caching, and throughput - Tune prompts, system messages, and grounding strategies - Fine-tune and distill models for specific use cases |
| Design and implement a GenAIOps infrastructure | - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
| Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints |
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
1. A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?
A) Increase timeout settings
B) Set minimum instance count
C) Switch to batch endpoint
D) Reduce logging
2. Hotspot Question
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2-based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
3. You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image.
A new version of the Docker image is available.
You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.
What should you do?
A) Use the create_or_update method to change the tag of the image.
B) Change the description parameter of the environment configuration.
C) Use the Environment class to create a new version of the environment.
D) Modify the conda_file to specify the new version of the Docker image.
4. You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?
A) Enable tracing for all inference calls in the evaluation pipeline.
B) Format the dataset as a JSONL file with prompt-completion pairs and upload the file.
C) Deploy the base model to an online endpoint before starting fine-tuning.
D) Use prompt flow to generate multiple prompt templates for evaluation.
5. You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data for multi-turn chat.
Which file encoding method should you use?
A) UTF-8
B) UTF-16
C) ASCII
D) ISO-8859-1
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: Only visible for members | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: A |
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